Ag-low and -high cells were identified by separatly clustering each LEC subset for each sample into two groups based on Ag-score. Ag-low/high classifications used for the analysis are shown below.
A random forest classifier was trained using data for d14 cLECs. The model was then used to predict Ag-high cells in the other Ag datasets.
The fraction of cells belonging to each predicted Ag group is shown on the left for cLECs from each sample. The fraction of cells incorrectly predicted to be Ag high (high-pred) is shown on the right for each sample.
The top positive (top) and negative (bottom) gene modules that are most predictive of Ag signal are shown below.
UMAP projections show Ag-high module expression for each sample, the main cLEC cluster is circled.
UMAP projections show true Ag-low and true Ag-high cLECs.
UMAP projections show false positive Ag-high and true Ag-high cLECs.
Mean expression in cLECs is shown on the left for genes from the Ag-high module for true Ag-low, true Ag-high, and false positive Ag-high (predicted, high-pred) cells. Expression is shown on the right for select top features.
Points show median expression, grey bars show interquartile range, dotted line shows the trend, and arrows indicate the gene is significantly up or down regulated when compared to Ag-low cells.
Expression of the Ag-low module is shown as described above.
The fraction of cells predicted to be Ag-high (i.e. archiving competent) is shown below for each cell type. p-values < 0.05 are shown.
Expression of the Ag-high (top) and Ag-low (bottom) gene modules is shown below for each predicted Ag class for the 24 hpi timepoint.
Mean expression is shown for genes from the Ag-high module for mock- and CHIKV-infected mice from the 24 hpi timepoint.
## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
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## locale:
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## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
## [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
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## time zone: America/Denver
## tzcode source: system (glibc)
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## attached base packages:
## [1] tools grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] furrr_0.3.1 future_1.33.0 ranger_0.15.1
## [4] rsample_1.2.0 harmony_1.0.3 biomaRt_2.56.1
## [7] openxlsx_4.2.5.2 MetBrewer_0.2.0 rdrop2_0.8.2.1
## [10] ggtext_0.1.2 ggtrace_0.2.0 qs_0.25.5
## [13] vroom_1.6.3 M3Drop_1.26.0 numDeriv_2016.8-1.1
## [16] djvdj_0.1.0 gtools_3.9.4 clustifyrdata_1.1.0
## [19] here_1.0.1 presto_1.0.0 data.table_1.14.8
## [22] Rcpp_1.0.11 devtools_2.4.5 usethis_2.2.2
## [25] ComplexHeatmap_2.16.0 patchwork_1.1.3 scales_1.2.1
## [28] boot_1.3-28.1 clustifyr_1.12.0 mixtools_2.0.0
## [31] broom_1.0.5 colorblindr_0.1.0 colorspace_2.1-0
## [34] xlsx_0.6.5 RColorBrewer_1.1-3 ggrepel_0.9.3
## [37] cowplot_1.1.1 knitr_1.44 gprofiler2_0.2.2
## [40] SeuratObject_4.1.4 Seurat_4.4.0 ggforce_0.4.1
## [43] ggbeeswarm_0.7.2 lubridate_1.9.3 forcats_1.0.0
## [46] stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
## [49] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
## [52] ggplot2_3.4.3 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] IRanges_2.34.1 progress_1.2.2
## [3] urlchecker_1.0.1 nnet_7.3-19
## [5] goftest_1.2-3 Biostrings_2.68.1
## [7] rstan_2.26.23 vctrs_0.6.3
## [9] spatstat.random_3.1-6 RApiSerialize_0.1.2
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## [15] parallelly_1.36.0 MASS_7.3-60
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## [19] foreach_1.5.2 BiocGenerics_0.46.0
## [21] withr_2.5.1 xfun_0.40
## [23] ellipsis_0.3.2 survival_3.5-5
## [25] memoise_2.0.1 profvis_0.3.8
## [27] zoo_1.8-12 GlobalOptions_0.1.2
## [29] pbapply_1.7-2 entropy_1.3.1
## [31] Formula_1.2-5 prettyunits_1.2.0
## [33] KEGGREST_1.40.1 promises_1.2.1
## [35] httr_1.4.7 globals_0.16.2
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## [73] pillar_1.9.0 StanHeaders_2.26.28
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## [119] sass_0.4.7 RANN_2.6.1
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## [125] MatrixGenerics_1.12.3 foreign_0.8-84
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## [129] leiden_0.4.3 lifecycle_1.0.3
## [131] uwot_0.1.16 Biobase_2.60.0
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## [135] sessioninfo_1.2.2 backports_1.4.1
## [137] BiocParallel_1.34.2 timechange_0.2.0
## [139] gtable_0.3.4 rjson_0.2.21
## [141] ggridges_0.5.4 densEstBayes_1.0-2.2
## [143] progressr_0.14.0 parallel_4.3.1
## [145] jsonlite_1.8.7 bitops_1.0-7
## [147] bit64_4.0.5 Rtsne_0.16
## [149] spatstat.utils_3.0-3 zip_2.3.0
## [151] RcppParallel_5.1.7 bdsmatrix_1.3-6
## [153] jquerylib_0.1.4 loo_2.6.0
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## [161] rappdirs_0.3.3 glue_1.6.2
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## [165] rprojroot_2.0.3 gridExtra_2.3
## [167] igraph_1.5.1 R6_2.5.1
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## [171] labeling_0.4.3 xlsxjars_0.6.1
## [173] cluster_2.1.4 bbmle_1.0.25
## [175] pkgload_1.3.3 GenomeInfoDb_1.36.3
## [177] rstantools_2.3.1.1 DelayedArray_0.26.7
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## [181] htmlTable_2.4.1 xml2_1.3.5
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## [185] munsell_0.5.0 KernSmooth_2.23-21
## [187] htmlwidgets_1.6.2 fgsea_1.26.0
## [189] rlang_1.1.1 spatstat.sparse_3.0-2
## [191] spatstat.explore_3.2-3 remotes_2.4.2.1
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